Exact Structural Inference in Optimal Job-Search Models
研究静态最优工作搜寻模型中结构参数的精确后验分布，利用模型潜在结构模拟不完全观测下的后验分布，并开发重采样算法施加完全最优搜寻约束，用模拟数据展示方法。
Abstract This article is a study of the exact posterior distributions of parameters appearing in a stationary optimal job-search model I exploit the simple latent structure of the search model when all job offers are observed to simulate posterior distributions of structural parameters when the latent structure is imperfectly observed. These simulations enable me to show the exact, and unusual, shape of the job-search likelihood when the data are durations and accepted wages. I also develop an algorithm to resample simulated posterior distributions to impose on the model the implications of fully optimal, utility-maximizing search. The methods are illustrated using simulated data. KEY WORDS: Gibbs samplerImportance resamplingStructural econometrics